# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.

# Copyright 2019 Kakao Brain
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Arbitrary dependency between two autograd lanes."""
from typing import List, Tuple

import torch
from torch import Tensor

from .phony import get_phony

__all__: List[str] = []


def fork(input: Tensor) -> Tuple[Tensor, Tensor]:
    """Branches out from an autograd lane of the given tensor."""
    if torch.is_grad_enabled() and input.requires_grad:
        input, phony = Fork.apply(input)
    else:
        phony = get_phony(input.device, requires_grad=False)

    return input, phony


class Fork(torch.autograd.Function):
    @staticmethod
    def forward(ctx: "Fork", input: Tensor) -> Tuple[Tensor, Tensor]:  # type: ignore
        phony = get_phony(input.device, requires_grad=False)
        return input.detach(), phony.detach()

    @staticmethod
    def backward(ctx: "Fork", grad_input: Tensor, grad_grad: Tensor) -> Tensor:  # type: ignore
        return grad_input


def join(input: Tensor, phony: Tensor) -> Tensor:
    """Merges two autograd lanes."""
    if torch.is_grad_enabled() and (input.requires_grad or phony.requires_grad):
        input = Join.apply(input, phony)

    return input


class Join(torch.autograd.Function):
    @staticmethod
    def forward(ctx: "Join", input: Tensor, phony: Tensor) -> Tensor:  # type: ignore
        return input.detach()

    @staticmethod
    def backward(ctx: "Join", grad_input: Tensor) -> Tuple[Tensor, None]:  # type: ignore
        return grad_input, None